Tumor microenvironment (TME) cells constitute a vital element of tumor tissue. Increasing evidence has elucidated their clinicopathologic significance in predicting outcomes and therapeutic efficacy. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the TME. In this study, we comprehensively estimated the TME infiltration patterns of 1,524 gastric cancer patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathologic features of gastric cancer using two proposed computational algorithms. Three TME phenotypes were defined, and the TMEscore was constructed using principal component analysis algorithms. The high TMEscore subtype was characterized by immune activation and response to virus and IFNg. Activation of transforming growth factor b, epithelial-mesenchymal transition, and angiogenesis pathways were observed in the low TMEscore subtype, which are considered T-cell suppressive and may be responsible for significantly worse prognosis in gastric cancer [hazard ratio (HR), 0.42; 95% confidence interval (CI), 0.33-0.54; P < 0.001]. Multivariate analysis revealed that the TMEscore was an independent prognostic biomarker, and its value in predicting immunotherapeutic outcomes was also confirmed (IMvigor210 cohort: HR, 0.63; 95% CI, 0.46-0.89; P ¼ 0.008; GSE78220 cohort: HR, 0.25; 95% CI, 0.07-0.89; P ¼ 0.021). Depicting a comprehensive landscape of the TME characteristics of gastric cancer may, therefore, help to interpret the responses of gastric tumors to immunotherapies and provide new strategies for the treatment of cancers.

Recent advances in next-generation sequencing (NGS) technologies have triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to explore robust signatures for clinical translation is increasingly emphasized, and this is attributed to the clinical success of immune checkpoint blockades in diverse malignancies. However, effective tools for comprehensively interpreting multi-omics data are still warranted to provide increased granularity into the intrinsic mechanism of oncogenesis and immunotherapeutic sensitivity. Therefore, we developed a computational tool for effective Immuno-Oncology Biological Research (IOBR), providing a comprehensive investigation of the estimation of reported or user-built signatures, TME deconvolution, and signature construction based on multi-omics data. Notably, IOBR offers batch analyses of these signatures and their correlations with clinical phenotypes, long non-coding RNA (lncRNA) profiling, genomic characteristics, and signatures generated from single-cell RNA sequencing (scRNA-seq) data in different cancer settings. Additionally, IOBR integrates multiple existing microenvironmental deconvolution methodologies and signature construction tools for convenient comparison and selection. Collectively, IOBR is a user-friendly tool for leveraging multi-omics data to facilitate immuno-oncology exploration and to unveil tumor-immune interactions and accelerating precision immunotherapy.

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Tumour-infiltrating immune cells are a source of important prognostic information for patients with resectable colon cancer. We developed a novel immune model based on systematic assessments of the immune landscape inferred from bulk tumor transcriptomes of stage I–III colon cancer patients. The “Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT)” algorithm was used to estimate the fraction of 22 immune cell types from six microarray public datasets. The random forest method and least absolute shrinkage and selection operator model were then used to establish immunoscores for diagnosis and prognosis. By comparing immune cell compositions in samples of 870 colon cancer patients and 70 normal controls, we constructed a diagnostic model, designated the diagnostic immune risk score (dIRS), that showed high specificity and sensitivity in both the training [area under the curve (AUC) = 0.98, p < 0.001] and validation (AUC 0.96, p < 0.001) sets. We also established a prognostic immune risk score (pIRS) that was found to be an independent prognostic factor for relapse-free survival in every series (training: HR 2.23; validation: HR 1.65; entire: HR 2.01; p < 0.001 for all), which showed better prognostic value than TNM stage. In addition, integration of the pIRS with clinical characteristics in a composite nomogram showed improved accuracy of relapse risk prediction, providing a higher net benefit than TNM stage, with well-fitted calibration curves. The proposed dIRS and pIRS models represent promising novel signatures for the diagnosis and prognosis prediction of colon cancer.Electronic supplementary materialThe online version of this article (10.1007/s00262-018-2289-7) contains supplementary material, which is available to authorized users.

BackgroundIncreasing evidence has indicated an association between immune infiltration in gastric cancer and clinical outcome. However, reliable prognostic signatures, based on systematic assessments of the immune landscape inferred from bulk tumour transcriptomes, have not been established. The aim was to develop an immune signature, based on the cellular composition of the immune infiltrate inferred from bulk tumour transcriptomes, to improve the prognostic predictions of gastric cancer.MethodsTwenty‐two types of immune cell fraction were estimated based on large public gastric cancer cohorts from the Gene Expression Omnibus using CIBERSORT. An immunoscore based on the fraction of immune cell types was then constructed using a least absolute shrinkage and selection operator (LASSO) Cox regression model.ResultsUsing the LASSO model, an immunoscore was established consisting of 11 types of immune cell fraction. In the training cohort (490 patients), significant differences were found between high‐ and low‐immunoscore groups in overall survival across and within subpopulations with an identical TNM stage. Multivariable analysis revealed that the immunoscore was an independent prognostic factor (hazard ratio 1·92, 95 per cent c.i. 1·54 to 2·40). The prognostic value of the immunoscore was also confirmed in the validation (210) and entire (700) cohorts.ConclusionThe proposed immunoscore represents a promising signature for estimating overall survival in patients with gastric cancer.

Fibroblast growth factor 23 (FGF23) has been reported to induce left ventricular hypertrophy, but it remains unclear whether FGF23 plays a role in cardiac fibrosis. This study is attempted to investigate the role of FGF23 in post-infarct myocardial fibrosis in mice. We noted that myocardial and plasma FGF23 and FGF receptor 4 were increased in mice with heart failure as well as in cultured adult mouse cardiac fibroblasts (AMCFs) exposed to angiotensin II, phenylephrine, soluble fractalkine. Recombinant FGF23 protein increased active β-catenin , procollagen I and procollagen III expression in cultured AMCFs. Furthermore, intra-myocardial injection of adeno-associated virus-FGF23 in mice significantly increased left ventricular end-diastolic pressure and myocardial fibrosis, and markedly upregulated active β-catenin, transforming growth factor β (TGF-β), procollagen I and procollagen III in both myocardial infarction (MI) and ischemia/reperfusion (IR) mice, while β-catenin inhibitor or silencing of β-catenin antagonized the FGF23-promoted myocardial fibrosis in vitro and in vivo. These findings indicate that FGF23 promotes myocardial fibrosis and exacerbates diastolic dysfunction induced by MI or IR, which is associated with the upregulation of active β-catenin and TGF-β.

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The synergism between cardiomyogenesis and angiogenesis is essential for cardiac regeneration. Circular RNAs (circRNAs) play pivotal roles in cell growth and angiogenesis, but their functions in cardiac regeneration are not yet known. In this study, we investigated the role and underlying mechanisms of circRNA Hipk3 (circHipk3) in both cardiomyogenesis and angiogenesis during cardiac regeneration. We found that circHipk3 was overexpressed in the fetal or neonatal heart of mice. The transcription factor Gata4 bound to the circHipk3 promoter and increased circHipk3 expression. Cardiomyocyte (CM) proliferation in vitro and in vivo was inhibited by circHipk3 knockdown and increased by circHipk3 overexpression. Moreover, circHipk3 overexpression promoted coronary vessel endothelial cell proliferation, migration, and tube-forming capacity and subsequent angiogenesis. More importantly, circHipk3 overexpression attenuated cardiac dysfunction and decreased fibrotic area after myocardial infarction (MI). Mechanistically, circHipk3 promoted CM proliferation by increasing Notch1 intracellular domain (N1ICD) acetylation, thereby increasing N1ICD stability and preventing its degradation. In addition, circHipk3 acted as a sponge for microRNA (miR)-133a to promote connective tissue growth factor (CTGF) expression, which activated endothelial cells. Our findings suggested that circHipk3 might be a novel therapeutic target for preventing heart failure post-MI.